Classification of MRI under the Presence of Disease Heterogeneity using Multi-Task Learning: Application to Bipolar Disorder

Página do item simplificado
dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorWANG, Xiangyang
dc.contributor.authorZHANG, Tianhao
dc.contributor.authorCHAIM, Tiffany M.
dc.contributor.authorZANETTI, Marcus V.
dc.contributor.authorDAVATZIKOS, Christos
dc.date.accessioned2016-03-24T15:02:28Z
dc.date.available2016-03-24T15:02:28Z
dc.date.issued2015
dc.description.abstractHeterogeneity in psychiatric and neurological disorders has undermined our ability to understand the pathophysiology underlying their clinical manifestations. In an effort to better distinguish clinical subtypes, many disorders, such as Bipolar Disorder, have been further sub-categorized into subgroups, albeit with criteria that are not very clear, reproducible and objective. Imaging, along with pattern analysis and classification methods, offers promise for developing objective and quantitative ways for disease subtype categorization. Herein, we develop such a method using learning multiple tasks, assuming that each task corresponds to a disease subtype but that subtypes share some common imaging characteristics, along with having distinct features. In particular, we extend the original SVM method by incorporating the sparsity and the group sparsity techniques to allow simultaneous joint learning for all diagnostic tasks. Experiments on Multi-Task Bipolar Disorder classification demonstrate the advantages of our proposed methods compared to other state-of-art pattern analysis approaches.
dc.description.conferencedateOCT 05-09, 2015
dc.description.conferencelocalMunich, GERMANY
dc.description.conferencename18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
dc.description.indexWoS
dc.description.sponsorshipNIA NIH HHS
dc.identifier.citationMEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT I, v.9349, p.125-132, 2015
dc.identifier.doi10.1007/978-3-319-24553-9_16
dc.identifier.isbn978-3-319-24553-9; 978-3-319-24552-2
dc.identifier.issn0302-9743
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/13785
dc.language.isoeng
dc.publisherSPRINGER INT PUBLISHING AG
dc.relation.ispartofMedical Image Computing and Computer-Assisted Intervention - Miccai 2015, Pt I
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsrestrictedAccess
dc.rights.holderCopyright SPRINGER INT PUBLISHING AG
dc.subject.otherselection
dc.subject.otherregression
dc.subject.otherlasso
dc.subject.wosComputer Science, Artificial Intelligence
dc.subject.wosComputer Science, Interdisciplinary Applications
dc.subject.wosComputer Science, Theory & Methods
dc.subject.wosRadiology, Nuclear Medicine & Medical Imaging
dc.titleClassification of MRI under the Presence of Disease Heterogeneity using Multi-Task Learning: Application to Bipolar Disorder
dc.typeconferenceObject
dc.type.categoryproceedings paper
dc.type.versionpublishedVersion
dspace.entity.typePublication
hcfmusp.affiliation.countryEstados Unidos
hcfmusp.affiliation.countryChina
hcfmusp.affiliation.countryisocn
hcfmusp.affiliation.countryisous
hcfmusp.author.externalWANG, Xiangyang:Univ Penn, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA; Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA; Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China
hcfmusp.author.externalZHANG, Tianhao:Univ Penn, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA; Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA
hcfmusp.author.externalDAVATZIKOS, Christos:Univ Penn, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA; Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA
hcfmusp.contributor.author-fmusphcTIFFANY MOUKBEL CHAIM AVANCINI
hcfmusp.contributor.author-fmusphcMARCUS VINICIUS ZANETTI
hcfmusp.description.beginpage125
hcfmusp.description.endpage132
hcfmusp.description.volume9349
hcfmusp.origemWOS
hcfmusp.origem.wosWOS:000366205700016
hcfmusp.publisher.cityCHAM
hcfmusp.publisher.countrySWITZERLAND
hcfmusp.relation.referenceCaruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023/A:1007379606734
hcfmusp.relation.referenceZhou JY, 2013, NEUROIMAGE, V78, P233, DOI 10.1016/j.neuroimage.2013.03.073
hcfmusp.relation.referenceYuan M, 2006, J ROY STAT SOC B, V68, P49, DOI 10.1111/j.1467-9868.2005.00532.x
hcfmusp.relation.referenceTibshirani R, 1996, J ROY STAT SOC B MET, V58, P267
hcfmusp.relation.referenceSimon N, 2013, J COMPUT GRAPH STAT, V22, P231, DOI 10.1080/10618600.2012.681250
hcfmusp.relation.referenceMetsis V, 2014, IEEE ACM T COMPUT BI, V11, P168, DOI 10.1109/TCBB.2013.141
hcfmusp.relation.referenceDUNNER DL, 1976, BIOL PSYCHIAT, V11, P31
hcfmusp.relation.referenceWang H, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P557
hcfmusp.relation.referenceAzadi S., 2014, ICML, P620
hcfmusp.relation.referenceCai X., 2011, ICDM, P91
hcfmusp.relation.referenceChang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI 10.1145/1961189.1961199
hcfmusp.relation.referenceEvgeniou A., 2007, NIPS, P41
hcfmusp.relation.referenceJie B, 2015, HUM BRAIN MAPP, V36, P489, DOI 10.1002/hbm.22642
hcfmusp.relation.referenceNie F., 2010, NIPS, P1813
hcfmusp.relation.referenceRao N., 2013, NIPS, P2202
hcfmusp.relation.referenceScholkopf B, 2002, LEARNING KERNELS SUP
hcfmusp.relation.referenceSerpa M.H., 2014, BIOMED RES INT
relation.isAuthorOfPublication8895fec3-75e6-410b-a0b3-e2fe014ad865
relation.isAuthorOfPublicationc0e3a2b2-9a34-4116-9da9-75bec7265484
relation.isAuthorOfPublication.latestForDiscovery8895fec3-75e6-410b-a0b3-e2fe014ad865
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
art_WANG_Classification_of_MRI_under_the_Presence_of_Disease_2015.PDF
Tamanho:
2.71 MB
Formato:
Adobe Portable Document Format
Descrição:
publishedVersion (English)
Baixar
Coleções
Comunicações em Eventos - FM/Outros
Comunicações em Eventos - LIM/21